Planar Data Classification with One Hidden Layer
This project was completed as a part of the Honors portion of the Neural Networks and Deep Learning Course on Coursera.
Credit to DeepLearning.AI and the Coursera platform for providing the course materials and guidance.
In this task, I will be building my very first neural network with a single hidden layer, which represents a significant difference from the previous implementation using logistic regression. The main objective of this assignment is to create a 2-class classification neural network with one hidden layer, incorporating units with a non-linear activation function like tanh to enhance its capabilities.
Throughout this assignment, I will focus on computing the cross-entropy loss, a critical aspect of the training process, and mastering the implementation of both forward and backward propagation. These steps are essential for effectively training the neural network.
